144 results about "Sentence segmentation" patented technology

The invention discloses a domain speech recognition method and system based on RAG. The method comprises the steps that an original domain document set is acquired, a hierarchical document structure is constructed through subject classification, paragraph segmentation and sentence segmentation, vectorization processing is executed to generate a multi-granularity vector matrix set, and a hierarchical knowledge vector index structure is established; performing multi-scale time-frequency analysis on the real-time audio stream, extracting and enhancing acoustic features, and generating a multi-scale feature tensor; performing adaptive blocking and progressive attention processing on the feature tensor, constructing a probability estimation model, and generating a candidate text sequence; executing multi-layer retrieval in the knowledge index based on the multi-dimensional query vector to generate related knowledge fragments; and carrying out space mapping and dynamic attention processing on the features, and executing probability optimization decoding to obtain a final recognition text. According to the invention, through knowledge enhancement and multi-level feature processing, the accuracy and robustness of voice recognition in the professional field are improved.

The invention discloses a construction method of a real-time streaming voice intelligent question and answer service system. The construction method comprises the following steps: receiving input voice data; calling an independent voice recognition service to convert the voice data into an input text; the method comprises the following steps: constructing cue words of a large language model based on an input text, initiating an LLM streaming request to an independent large language model service, and obtaining streaming text answers generated by LLM in real time; segmenting the flow type text answer in real time through a dynamic sentence segmentation device to generate a plurality of clauses; calling an independent speech synthesis service in parallel for each clause, and converting the text into an audio data block; and combining the audio data blocks into streaming audio data according to the generation sequence, and returning the streaming audio data to a client for playing in real time. According to the invention, by decoupling ASR, LLM and TTS services and combining the asynchronous streaming framework and the strategy of "wanting while speaking", the real-time performance and flexibility of the voice question-answering system are significantly improved.

The invention discloses an event association analysis method and system based on a large language model and a pre-training model, and the method comprises the steps: carrying out the sentence segmentation and segmentation processing of each event, inputting the processed events into a large language model, and generating the sentence representation of an event pair; aggregating global expressions of paragraphs and sentence levels of the event by the large language model; retrieving a semantic paragraph related to the current event; splitting the event reasoning task into a plurality of steps, wherein each step corresponds to a specific reasoning task in event association analysis; generating a possible reasoning path, and performing marginalization processing on the reasoning path; the multiple reasoning paths are evaluated at the same time, the most reasonable reasoning result is obtained and serves as input, and a final classification result is generated through weighted fusion; and according to a classification result, determining a specific relationship type between the event pairs, and outputting a determination result of a causal, sequential or hyponymy relationship between the events. According to the method, the complex relationship between events can be more accurately captured.

The embodiment of the invention belongs to the technical field of speech translation, is applied to a real-time speech translation scene, and relates to a translation method, device and equipment and a storage medium thereof. Text conversion is carried out; inputting a textualized conversion result into a sentence segmentation prediction model, and performing sentence segmentation prediction; receiving all output sentences; performing text translation on all sentences according to the target language; according to the sequence information of all sentences in the textualized conversion result, performing serialized arrangement on the text translation result; and converting the text translation result into target voice data. According to the translation method provided by the invention, each sentence in the real-time voice data to be translated can be accurately segmented through the sentence segmentation prediction model, so that subsequent translation by taking the sentence as a unit is realized, the target voice data is generated, and the translated voice is more accurate and scientific.

The invention belongs to the technical field of power equipment operation inspection auxiliary decision making, and particularly relates to a power operation inspection knowledge service method and device, equipment and a medium. The method comprises the following steps: analyzing semantics of query input of a user by utilizing a large semantic model, and determining a corresponding target sub-knowledge base in combination with an intention classification sample library and an intention set; performing sentence segmentation on the document of the target sub-knowledge base, generating document fragments containing continuous sentences through context supplementation, and enabling semantics of the document fragments to be overlapped; calculating a keyword correlation score of the query word and the document fragment; generating a text vector through a semantic coding model, and calculating a semantic correlation score of the query input and the document fragments; fusing the keyword correlation score and the semantic correlation score, and outputting each related document fragment according to a comprehensive score sequence; and inputting query input of a user and related document fragments into the large semantic model, and generating a structured answer in combination with the logic in the operation and inspection field of the power equipment. And the electric power operation and maintenance knowledge service with high accuracy and strong generalization is realized.

The invention provides a tunnel risk reasoning method fusing a knowledge graph and a large language model, which comprises the following steps of: obtaining structured monitoring data and unstructured text data, adopting methods such as field standardization for the structured monitoring data to realize a unified format, adopting methods such as sentence segmentation and word segmentation for the unstructured text data to realize the unified format, and obtaining the structured monitoring data and the unstructured text data; the method comprises the following steps of: extracting entities from data by utilizing a model, extracting a relationship between the entities based on the entities, forming basic triads, forming a sub-graph by the basic triads, integrating to form a knowledge graph, generating a natural language, extracting the sub-graph related to the natural language from the knowledge graph, and converting the sub-graph into a sub-graph in a vector form by utilizing a graph embedding algorithm. The entities and the relation paths of the entities serve as explicit reasoning clues, the natural language, the sub-maps in the vector form and the explicit reasoning clues are input into a large language model, natural language output is generated, multi-source data information is integrated, and high-precision and interpretable tunnel risk early warning is output through the large language model.

A minutes determining method is provided. The method includes acquiring a to-be-used user record and a to-be-used record text; performing sentence segmentation processing on the to-be-used record text, to obtain at least one to-be-used sentence; performing semantic matching processing on the to-be-used user record and the at least one to-be-used sentence, to obtain a to-be-used semantic matching result; and determining to-be-used minutes content according to the to-be-used user record and the to-be-used semantic matching result.

The invention provides a medical record information extraction and analysis method and system based on a deep learning large model, and the method comprises the steps: carrying out the semantic understanding of a medical record text through a deep learning model, and recognizing key clinical features; extracting the key clinical features, and carrying out vectorization processing on the extracted key clinical features; performing sentence segmentation and word segmentation on the case text based on a rule engine and the key clinical features; adjusting the segmented words based on a symptom classification rule; and carrying out word frequency statistics on the adjusted segmented words. According to the method, accurate recognition and structured conversion of key clinical features can be ensured through a semantic understanding technology, knowledge graph construction and machine learning model optimization are supported, and a new view angle and a new tool are provided for exploration and research of diseases.

The invention discloses a multi-language speech recognition and interaction method of a humanoid robot, particularly relates to the technical field of speech recognition and interaction, and effectively separates background noise and target speech signals through a multi-channel high-sensitivity microphone array in combination with an adaptive noise elimination algorithm, thereby improving the speech signal recognition accuracy in a noisy environment and improving the speech recognition efficiency. A speech speed fluctuation abnormal coefficient, an intonation deviation coefficient and a background noise influence coefficient are calculated through deep analysis of speech signals and background noise feature data of a target interaction object, and a sentence segmentation model is adjusted according to habits of speech speeds, intonation, pauses and the like of different speakers, so that the problem of inaccurate sentence segmentation under different users or contexts is avoided; the sentence segmentation adaptation optimization index is generated according to the sentence segmentation adaptation optimization model, whether parameters of the sentence segmentation model need to be adjusted or not is judged by combining a threshold value, real-time response and optimization are achieved, the situation of misjudgment or missed judgment is reduced, and the smoothness and accuracy of overall interaction are improved.

The invention discloses a method and system for jointly extracting entity and relation information in a text, and the method comprises the steps: inputting a text sentence, segmenting the sentence into lexical elements, and obtaining a lexical element sequence; inputting the lexical element sequence into an ALBERT model to generate a first output vector; inputting the lexical element sequence into a TKGTransE knowledge graph embedding model to generate a second output vector; inputting the first output vector and the second output vector into a GFN gating fusion network to generate a first vector representation; and inputting the first vector representation and the lexical element sequence into a non-autoregression decoder to generate a prediction triple, and evaluating a prediction result by adopting a bipartite matching loss function to generate a prediction vector. According to the method, knowledge fusion is realized, and the processing capability of the model on a complex text structure is improved.

The invention discloses a voice sentence segmentation detection method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring first voice basic data of a first voice and second voice basic data of a second voice; determining a second mute duration threshold value according to the first voice basic data and the second voice basic data; and determining a first sentence segmentation detection result of the second voice according to the mute duration of the second voice and the second mute duration threshold, wherein the first sentence segmentation detection result is used for indicating whether the second voice needs sentence segmentation or not. A mute duration threshold is dynamically adjusted according to second voice basic data of voice needing sentence segmentation detection and first voice basic data of voice already subjected to sentence segmentation detection, then voice sentence segmentation detection is performed according to the second voice basic data and the adjusted mute duration threshold, and a voice sentence segmentation detection result is obtained. And the accuracy of voice sentence segmentation detection can be improved.

The invention discloses a knowledge graph construction method based on a long and short term memory network introducing an attention mechanism, and the method specifically comprises the steps: S1, carrying out the sentence segmentation and word segmentation preprocessing of a data set, including the removal of noise, special characters and stop words; s2, performing BIOES label labeling on the data after word segmentation, and introducing a hierarchical relationship; s3, word vector representation is established, a BERT model is introduced to capture context information, and deep semantic understanding is provided for word vectors; s4, establishing a Bi-LSTM neural network layer, obtaining a score probability of each word corresponding to each tag by using the Bi-LSTM neural network layer, adding an attention mechanism behind the Bi-LSTM layer, and capturing a long-distance dependency relationship; s5, establishing a CRF-GNN layer, and obtaining an output labeling sequence with the maximum probability; and S6, carrying out post-processing on the extracted information. According to the method, the Bi-LSTM model and the CRF model are fused, corresponding constraints can be given to the tag sequence, and the problem that information extraction output logic is disordered is solved.

The invention provides a semantic block-based retrieval method and system, electronic equipment and a storage medium, and belongs to the field of artificial intelligence. The method comprises the following steps: segmenting a text material into sentences and coding the sentences into vectors; calculating cosine similarity between adjacent sentences, determining block boundaries according to a similarity threshold, combining a plurality of sentences belonging to the same block boundary into blocks, and coding; rewriting the query input by the user by using an LLM model, generating a relevancy score, and removing a block lower than a score threshold to obtain a target block; and performing keyword retrieval in the target block by using a BM25 retriever, reordering and combining a result and the target block into a context, and sending the context and user query into an LLM (Logistics Language Model) to generate an answer. According to the method, the numerical vector representation is generated through sentence segmentation, and the cosine similarity between the sentence vectors is calculated, so that the start of the new topic can be identified more accurately, the boundary of the block is determined, the organization and retrieval efficiency of information is optimized, and the accuracy of a retrieval result is ensured.

The invention discloses a silent time window prediction method and device, a storage medium and an electronic device, and relates to the technical field of smart home, the method comprises the following steps: obtaining historical voice interaction data of a target object, the historical voice interaction data being used for indicating an interaction voice of the target object and a voice device; determining a first time difference between voice interaction time corresponding to historical voice interaction data and current time, and determining first sentence segmentation interval time corresponding to the historical voice interaction data; determining a target weight corresponding to the historical voice interaction data according to the first time difference; and predicting a target silence time window of interaction between the target object and the voice device according to the target weight and the first sentence segmentation interval time. Through the method, the problem that the overall voice interaction duration is increased due to the fact that a long silent time window is selected to adapt to different sentence segmentation habits of different user groups during speaking in the related technology can be solved.

A public opinion analysis visualization platform construction method based on sentiment analysis belongs to the technical field of natural language processing, and specifically comprises the following steps: 1, collecting related corpora and data required by sentiment analysis; 2, preprocessing comment data required by sentiment analysis; and filtering invalid texts, and performing sentence segmentation processing. 3, firstly performing text classification on the processed data, then performing sentiment analysis, judging sentiment tendency in a comment text, and finally performing visual display; and 4, performing public opinion early warning based on an obtained sentiment analysis result and a popularity data analysis result. And 5, generating an independent public opinion evaluation report and a summary report through a related API interface of the local large language model. Through the method, the public opinion response efficiency can be remarkably improved, the risk identification capability is enhanced, the complex language understanding bottleneck is broken through, and meanwhile the manual analysis cost is reduced.

The invention provides a false news detection method and device and terminal equipment, and is suitable for the technical field of data processing.The method comprises the steps that statement segmentation and invalid statement filtering processing are conducted on news text information to be detected, and multiple pieces of news statement information to be detected are obtained; generating multiple pieces of to-be-detected news text structured information according to the to-be-detected news text information, the multiple pieces of to-be-detected news statement information, preset to-be-detected news text extraction guide information and a preset to-be-detected news text structured information extraction model; and generating multiple pieces of news text detection information according to the to-be-detected news text information, the multiple pieces of to-be-detected news text structured information and a preset news text detection model. According to the method, the requirements of scenes such as manual review and judicial evidence collection on interpretability and traceability are met, and the accuracy, reliability and engineering reproducibility of false news detection in a complex context are remarkably improved.

The invention discloses a voice recognition method and device based on a large language model, equipment and a medium, the method is applied to a management server, and the method comprises the following steps: inputting a received real-time input audio stream into a preset stream recognition model to obtain a corresponding one-stage voice recognition text; according to the determined sentence segmentation points, sequentially segmenting the audio stream into a plurality of segments of sub-audio streams according to a time sequence, and sequentially inputting all the sub-audio streams into a preset first speech recognition model to obtain a corresponding two-stage sub-speech recognition text; and inputting the two-stage sub-speech recognition texts related to the different first sentence segmentation points into a preset large language model in batches to obtain a plurality of corresponding corrected two-stage sub-speech recognition texts so as to obtain a target speech recognition text corresponding to all the received audio streams. According to the method, the accuracy of voice recognition can be improved, the sentence segmentation position is corrected and optimized through the large language model, and the reduction degree of voice content is improved.

A method and a system for assessing sentence segmenting in subtitles of a digital content is disclosed. The method comprises of acquiring a source text of the digital content and identifying linguistic boundary within sentences by assigning parts of speech (POS) tags and dependency tags to each word using natural language processing (NLP) libraries. Further, head information is assigned for each word to form a dependency tree structure. And, then, cohesiveness scores are assigned based at least on the parts of speech (POS) tags and the dependency tree structure. The method further includes identifying incorrect lines which violate the linguistic boundary and a set of static rules, and thereby assessing the sentence segmentation in subtitles of a digital content.

The invention discloses an ancient Chinese automatic sentence segmentation and semantic analysis system based on deep learning, and relates to the technical field of ancient Chinese analysis, according to the ancient Chinese automatic sentence segmentation and semantic analysis system based on deep learning, a sentence segmentation module is fused with a gated loop unit (GRU) and a convolutional neural network (CNN), and image font features are combined, so that a sentence segmentation result is obtained; the problems of semantic ambiguity, long-distance dependence, common and false characters and abnormal characters can be effectively solved; a knowledge graph containing historical and cultural common knowledge and vocabulary dynamic relations is constructed through a semantic analysis module, prior knowledge is injected through a graph neural network, and the semantic understanding depth is improved; a sentence segmentation result containing a sentence segmentation basis and a visual semantic analysis result are provided through an output module, so that a user can clearly master processing logic, and credibility and convenience are improved; a large number of unlabeled ancient Chinese texts are efficiently processed by the system, and values of various cultural heritage such as classics and inscriptions are fully mined; the development of grammar and vocabulary research is promoted, and ancient Chinese culture inheritance and propagation are assisted.

The invention provides a document analysis method and device based on a large model agent, and the method comprises the steps: generating a first sentence element list of a target document based on the document type of the target document; generating a second sentence element list based on a large model agent and the abnormal sentence segmentation symbols in the first sentence element list; and sorting the second sentence element list, and based on the sorted second sentence element list, generating a document segmented sentence list as a document analysis result of the target document. According to the document analysis method and device based on the large model agent, the sentences which possibly have abnormal segmented sentences due to nonstandard documents can be quickly and effectively judged and processed, and subsequent text processing such as translation and model training is avoided; the method solves the problem of poor precision effect caused by sentence integrity under the conditions of sentence integrity, content segmentation and the like, and can efficiently save the cost of front manual review and rear manual review at low cost.

The invention discloses an intelligent equipment fault relation extraction method based on multi-semantic knowledge interaction and dynamic pruning. Performing word segmentation and sentence segmentation on the original equipment fault text to obtain a statement set; obtaining a character-level word embedding representation set of the text by using a pre-training language model; extracting fault entity representations, obtaining fault type representations based on the constructed fault type knowledge base, constructing semantic association between the fault type representations, and generating a context feature representation sequence; calculating a correlation score between the enhanced semantic representation and an original text statement based on a dot product attention mechanism, dynamically selecting a core statement set, focusing the core statement, updating the semantic representation, and inputting the semantic representation into a relationship classification module to predict a relationship type between the entity pairs; and finally, outputting a named entity boundary and a relation label based on conditional random field decoding to obtain a final relation extraction result. According to the method, the ability of understanding the context is enhanced, and the fault relation extraction task under the complex context can be better processed.

The invention provides an interface recommendation method and device, equipment, a medium and a program product, and relates to the field of big data. The method comprises the steps of performing sentence segmentation on a business demand document, performing abstract extraction on a sentence segmentation result of the business demand document based on a plurality of preset interface documents to obtain a business demand document abstract, and dividing each interface document into a corresponding service set under each classification level according to a service category to which each interface belongs under a plurality of classification levels. Determining a keyword of each service set under each classification level, and determining a service category corresponding to the business demand document and a corresponding recommendation interface based on the keyword of the business demand document abstract, the keyword of each service set and the keyword of each interface document; according to the method, accurate identification of the service category corresponding to the business demand document and intelligent matching of the recommendation interface are realized, and the efficiency and accuracy of matching the service interface of the business demand document are improved.

The invention relates to the field of intelligent interaction, and provides a production command case-oriented interaction deduction method and system, and the method employs a text analysis and processing technology based on a deep learning model to carry out the sentence segmentation and semantic coding of the text description of a production command case, carries out the semantic coding of the text description of a current trigger event, and carries out the semantic coding of the text description of the current trigger event. In this way, the auxiliary prompt content is automatically generated according to the query matching representation between the text semantic information of the current trigger event and the sentence granularity description semantics of each production command case. Therefore, the production command case fragment most related to the current event can be identified more accurately, and more accurate and personalized auxiliary prompts are dynamically generated according to the current condition, so that power grid production command training and emergency drilling are better supported.

This application discloses an intelligent method and apparatus for detecting active speech with sentence segmentation based on multi-state temporal modeling. The method includes: receiving audio signals from at least one channel; extracting acoustic feature sequences from the audio signals using a target speech recognition model corresponding to the number of channels; determining the probability distribution of each speech frame corresponding to the acoustic feature sequences belonging to different speech activity states, obtaining a state sequence corresponding to each channel, wherein the speech activity state includes at least one of the following: initial silence state, speech state, intra-turn pause silence state, and inter-turn sentence segmentation silence state; and determining the time of sentence segmentation in the audio signal based on the state sequence. This application solves the technical problem of erroneous sentence segmentation in speech activity detection based on a fixed silence threshold in related technologies.

The invention provides an intelligent processing method and system from structured data to a text based on a natural language, and relates to the technical field of data processing.The method comprises the steps that the text chunk analysis process is optimized and adjusted according to analysis optimization parameters, sentences are segmented into non-overlapping phrases with syntactic function labels, and the text after chunk analysis is obtained; performing syntactic and semantic structure analysis on the text subjected to block analysis, and establishing a semantic association relationship between internal structures of sentences through component analysis, dependency analysis and semantic dependency graph analysis to obtain structured semantic information; performing multi-sentence logic association analysis on the structured semantic information on a chapter level to obtain semantic information of the whole chapter; and based on the semantic information of the overall chapter, obtaining a target natural language text by utilizing a pre-trained large language model. According to the method, the accuracy and fluency of conversion from the structured data to the text are improved.

The present invention relates to the technical field of language translation, and specifically to a method and system for optimizing translation accuracy based on artificial intelligence, including: obtaining a source language text, performing a sentence segmentation operation on the source language text to obtain a source language sentence sequence of the source text, and performing a word segmentation operation on each source language sentence in the source language sentence sequence to obtain a word sequence of the source language sentence; performing sentiment classification on each source language sentence in the source language sentence sequence based on a pre-trained first sentiment classification model. The present invention performs sentiment classification on the source language sentence and the target language sentence respectively based on the first sentiment classification model and the second sentiment classification model, and calculates the similarity between them to ensure that the translated sentence is consistent in sentiment, which helps to avoid the problem of sentiment deviation in the process of machine translation, especially for some texts with strong sentiment colors, and ensures that the sentiment information of the translation can be conveyed correctly.

The invention discloses an intelligent voice sentence segmentation method and system based on multi-modal fusion. The method comprises the following steps: acquiring voice data to be processed; processing the voice data by using a pre-trained acoustic sentence segmentation model to obtain an acoustic time sequence feature vector; processing the voice data by using a pre-trained text semantic model to obtain a semantic feature vector; and performing cross attention fusion on the acoustic time sequence feature vector and the semantic feature vector to obtain a fusion result, and determining a sentence segmentation result corresponding to the voice data according to the fusion result. According to the method and the device, the technical problem that the sentence segmentation position cannot be accurately determined due to the fact that acoustic features and semantic information are not fully combined in a related sentence segmentation method is solved.

The invention provides a text information recognition method and device, equipment and a storage medium. In some embodiments of the present disclosure, a first paragraph text and a second paragraph text of an audit report text are obtained; performing sentence segmentation processing on the first paragraph text and the second paragraph text to obtain a first sentence of the first paragraph text and a second sentence of the second paragraph text; combining the first clause and the second clause pairwise to obtain a sentence pair; encoding each sentence pair to obtain a sentence vector corresponding to each sentence pair; performing semantic emotion recognition on the sentence vector corresponding to each sentence pair to obtain a consistency result of each sentence pair; determining consistency information of the first paragraph text and the second paragraph text according to a consistency result of each sentence pair; based on semantic emotion recognition in the field of natural language processing, the audit report text is subjected to consistency auditing automatically, the labor cost is reduced, the auditing efficiency is improved, and the auditing accuracy is improved.

The application provides a role-aware chapter theme event argument extraction method and device, and the method comprises the following steps: obtaining argument role information of a chapter theme event of an event type according to the event type; performing sentence segmentation and title extraction on a target article to obtain a sentence set and an event title; the argument role information, the event type, and the event title constitute event-related information; constructing an argument role-aware graph by using the event-related information and the sentence set, performing event-related sentence detection, and obtaining a chapter theme event-related sentence set; taking the chapter theme event-related sentence set as input, constructing a question for each argument role, predicting all candidate arguments in the chapter theme event-related sentence set, and screening out a target argument from the candidate arguments. The method improves the model effect while maintaining the flexibility of the model.

The invention relates to the field of intelligent operation and maintenance, and relates to a knowledge extraction method for intelligent operation and maintenance. The method comprises the steps of obtaining a to-be-recognized text; performing sentence segmentation and word segmentation processing on each statement in the text to obtain a plurality of named entities; marking the entity type of each named entity, if a nested entity exists in the statement, calculating the probability that each nested entity belongs to each entity type to obtain a statistical result, and determining the entity type of each nested entity according to the statistical result; the marked named entities are input into a relation extraction model, the relation extraction model carries out relation extraction on all the named entities in the text, and relation information between the entities is obtained; extracting attribute information of each named entity in the text; and storing the entity type, the attribute information and the relationship information between the entities of each named entity in the text to complete knowledge extraction. The invention further provides a knowledge extraction system and device for intelligent operation and maintenance and a storage medium. The accuracy of the knowledge extraction result can be improved.